Tag Archives: science

Influential people + influential friends = spread products

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Identifying social influence in networks is critical to understanding how behaviors spread. We present a method for identifying influence and susceptibility in networks that avoids biases in traditional estimates of social contagion by leveraging in vivo randomized experimentation. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product we studied. Analysis of influence and susceptibility together with network structure reveals that influential individuals are less susceptible to influence than non-influential individuals and that they cluster in the network, which suggests that influential people with influential friends help spread this product [red text highlighting added].

Identifying Influential and Susceptible Members of Social Networks
Sinan Aral, Dylan Walker

Science http://dx.doi.org/10.1126/science.1215842

Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology—the network of friendships—and dynamics—the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latter’s success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011

Locating privileged spreaders on an online social network

Javier Borge-Holthoefer, Alejandro Rivero, and Yamir Moreno

Phys. Rev. E 85, 066123 (2012)

http://link.aps.org/doi/10.1103/PhysRevE.85.066123

Who believes in the 90-9-1 rule?

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A second question on LinkedIn from Dr Michael Wu, Principal Scientist at Lithium Technologies:

Is there something more accurate and precise than the 90-9-1 rule out there? IMHO, Lorenz Curve and Gini Coefficient. Do you know anything else? The Economics of 90-9-1

My answer as part of yesterday’s Online Community Manager group discussion kind of sums up where I’ve got to after reading Dr Wu’s blog previous post and this latest one:

I like the approach you have using economics-based models. I’ve come at it from a more particpant-observer type sociological point of view, so what I’d like to see is for your analysis to return a new ‘rule of thumb’ based on your in-depth data analysis.

The 90-9-1 rule is useful to community managers because it helps provides a starting point for understanding, as Arantza says above. For example it would be useful to know from a practical point of view whether for more open communities (as opposed to niche market research or project based communities) the 90-9-1 is a useful tool for helping launch a new community.

It’s partly about creating a social dashboard that can explain to a member of senior management why a certain kind of community activity may help or hinder greater participation.

I did this kind of work previously in the National Health Service, creating simple reports on the success of a national public health initiative, which worked well for senior managers (government ministers in that case).

So I come back to the challenge, the age old relationship between lab & fieldwork if you like, what would be the new rule of thumb/thumbs?

I’ve chosen to highlight multiple feedback loops as a useful tool, to help drive top contributors for example (taken from the HP Labs research), but I take your point that for commercial ROI purposes more precision is required. To put it another way in such a dynamic social context how does precision allow you to create heuristics for day to day community management?

Systemantics and online communities

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OK, it’s long list but it’s pretty useful when thinking of designing online communities for example! From John Gall. So as a planning tool how about thinking where your approach might fit into these. Good or bad!

1. The Primal Scenario or Basic Datum of Experience: Systems in general work poorly or not at all. (Complicated systems seldom exceed five percent efficiency.)
2. The Fundamental Theorem: New systems generate new problems.
3. The Law of Conservation of Anergy [sic]: The total amount of anergy in the universe is constant. (“Anergy” = ‘human energy’)
4. Laws of Growth: Systems tend to grow, and as they grow, they encroach.
5. The Generalized Uncertainty Principle: Systems display antics. (Complicated systems produce unexpected outcomes. The total behavior of large systems cannot be predicted.)
6. Le Chatelier’s Principle: Complex systems tend to oppose their own proper function. As systems grow in complexity, they tend to oppose their stated function.
7. Functionary’s Falsity: People in systems do not actually do what the system says they are doing.
8. The Operational Fallacy: The system itself does not actually do what it says it is doing.
9. The Fundamental Law of Administrative Workings (F.L.A.W.): Things are what they are reported to be. The real world is what it is reported to be. (That is, the system takes as given that things are as reported, regardless of the true state of affairs.)
10. Systems attract systems-people. (For every human system, there is a type of person adapted to thrive on it or in it.) [eg: watch out for contributors who dominate your community]
11. The bigger the system, the narrower and more specialized the interface with individuals.
12. A complex system cannot be “made” to work. It either works or it doesn’t.
13. A simple system, designed from scratch, sometimes works.
14. Some complex systems actually work.
15. A complex system that works is invariably found to have evolved from a simple system that works.
16. A complex system designed from scratch never works and cannot be patched up to make it work. You have to start over, beginning with a working simple system.
17. The Functional Indeterminacy Theorem (F.I.T.): In complex systems, malfunction and even total non-function may not be detectable for long periods, if ever.
18. The Newtonian Law of Systems Inertia: A system that performs a certain way will continue to operate in that way regardless of the need or of changed conditions.
19. Systems develop goals of their own the instant they come into being.
20. Intrasystem [sic] goals come first.
21. The Fundamental Failure-Mode Theorem (F.F.T.): Complex systems usually operate in failure mode.
22. A complex system can fail in an infinite number of ways. (If anything can go wrong, it will.) (See Murphy’s law.)
23. The mode of failure of a complex system cannot ordinarily be predicted from its structure.
24. The crucial variables are discovered by accident.
25. The larger the system, the greater the probability of unexpected failure.
26. “Success” or “Function” in any system may be failure in the larger or smaller systems to which the system is connected.
27. The Fail-Safe Theorem: When a Fail-Safe system fails, it fails by failing to fail safe.
28. Complex systems tend to produce complex responses (not solutions) to problems.
29. Great advances are not produced by systems designed to produce great advances.
30. The Vector Theory of Systems: Systems run better when designed to run downhill.
31. Loose systems last longer and work better. (Efficient systems are dangerous to themselves and to others.)
32. As systems grow in size, they tend to lose basic functions.
33. The larger the system, the less the variety in the product.
34. Control of a system is exercised by the element with the greatest variety of behavioral responses.
35. Colossal systems foster colossal errors.
36. Choose your systems with care.